p3/preprocess/Obesity/code/GSE158850.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obesity"
cohort = "GSE158850"

# Input paths
in_trait_dir = "../DATA/GEO/Obesity"
in_cohort_dir = "../DATA/GEO/Obesity/GSE158850"

# Output paths
out_data_file = "./output/preprocess/3/Obesity/GSE158850.csv"
out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE158850.csv"
out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE158850.csv"
json_path = "./output/preprocess/3/Obesity/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on series title and design, this appears to be skeletal muscle transcriptome data
is_gene_available = True

# 2.1 Data Availability
# Trait: No specific obesity measurement values are provided
trait_row = None

# Age: Background info indicates having young and elderly groups, but not specific ages
age_row = None 

# Gender: Feature 1 shows mix of male/female
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not used since trait data not available
    return None

def convert_age(x): 
    # Not used since age data not available
    return None
    
def convert_gender(x):
    # Not used since gender data not available
    return None

# 3. Save metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing the mapping information columns
print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'GENE_SYMBOL' column: Contains gene symbol information")
# Create mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Verify the result
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows after mapping:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# 1. Normalize and save gene data
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Create empty DataFrame for validation since we lack clinical data
empty_df = pd.DataFrame() 

# Validate and save cohort info, marking as biased due to lack of trait data
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,
    df=empty_df,
    note="Study has gene expression data but lacks usable clinical trait information for obesity analysis"
)